Special Section on VCBM 2020Aneulysis – A system for the visual analysis of aneurysm data
Graphical abstract
Introduction
Cerebral aneurysms are pathological dilatations of intracranial arteries that bear a risk of rupture. Although most aneurysms will never rupture, the potential risk of bleeding makes their detection and risk assessment a critical issue. The decision to treat a non-ruptured aneurysm involves a patient-specific risk analysis [1].
Aneurysm initiation, evolution and rupture are caused by several factors, such as genetics, vessel morphology, inflammation, hemodynamics, and epidemiological factors. In clinical routine, only morphological features such as aneurysm location, size and shape determine the risk assessment and treatment planning [2]. Quantitative hemodynamic characteristics such as wall shear stress (WSS) or pressure as well as qualitative features, e.g., vortices, seem to influence the aneurysm state including wall stability and thrombus formation [1]. Therefore, information on patient-specific hemodynamics is needed.
Computational fluid dynamics (CFD) simulations and fluid-structure interaction (FSI) can model the patient-specific wall mechanics and hemodynamics [3]. Both methods result in time-dependent flow data, comprising scalar, vectorial and tensor-based information, representing one cardiac cycle. However, identification of risky correlations between morphological and hemodynamic features is time-consuming and challenging because of the complexity of the data. Gaining knowledge from simulated data requires customized analysis software that offers guided exploration through the different data types and is easy to use. Moreover, standardized data set evaluation is crucial to compare results from different research facilities.
Existing systems concentrate on specific aspects of aneurysm data analysis, such as wall thickness exploration [4], internal flow visualization [5], or treatment planning [6]. There are no systems that focus on the simultaneous analysis of wall and flow features supported by an objective classification of morphological and hemodynamic characteristics to improve the risk analysis and treatment planning. Therefore, we previously presented Aneulysis – an interactive management and visualization system to evaluate the rupture risk and treatment decisions of cerebral aneurysms [7]. Based on various non-web-based modules, forces acting on the vessel wall as well as the behavior of the internal blood flow can be analyzed simultaneously.
In this paper, we extend the previous work by integrating a web-based module to analyze findings from previous explorations. With this, Aneulysis offers the possibility to share exploration results with people who do not have access to the entire database, such as other experts, students or patients. Thus, it is not necessary to install the entire application locally, which is especially useful in clinical settings with existing installation restrictions.
Section snippets
Related work
The research of the rupture risk and treatment of cerebral aneurysms covers three main areas: the analysis of morphological aspects, the exploration of flow patterns, and the simultaneous investigation of morphological and hemodynamic aspects. While we focus on the analysis of cerebral data, Oeltze et al. [8] give an overview about the evaluation of medical flow data comprising cardiac, cerebral and nasal data. Moreover, we summarize approaches for web-based visual exploration and analysis of
Requirement analysis
We closely cooperated with two neuroradiologists (16 and 25 years of work experience), who regularly treat cerebral aneurysms, and two engineers working on CFD simulations for cerebral aneurysms (five and eight years of work experience). Neuroradiologists compare ruptured and non-ruptured cases to better understand risk factors and more reliably assess the rupture risk. In contrast, CFD engineers analyze fluid-wall interactions to validate the physical plausibility of the simulation results.
Data acquisition and pre-processing
This section describes the data acquisition and pre-processing to simulate the flow within cerebral aneurysms. In addition, necessary post-processing steps are explained.
Image acquisition. First, clinical images comprising CTA, MRA and DSA of the aneurysm morphology are acquired.
Surface reconstruction. Based on the image data, the vessel surface is reconstructed using the pipeline by Munch et al. [48]. The aneurysm and its parent vessel are separated from the surrounding tissue using a
Aneulysis – aneurysm analysis system
In this section, we shortly recap the non-web-based modules of Aneulysis. For a comprehensive description of the non-web-based modules, please refer to our previous work [7]. Furthermore, the web-based module to explore individual data sets as well as to share exploration results via the Internet is explained in detail.
Aneulysis consists of four components: a database, an FTP server, a web server, and a visualization component. To meet Req. 1, the acquired raw data and derived representations
Results
This section gives an overview of the implementation of Aneulysis including the WADE module, see Section 6.1. In addition, we briefly summarize a conducted case study and informal evaluation with user feedback of the non-web-based application part, see Section 6.2 and Section 6.3. More details regarding these evaluations can be found in our previous work [7]. Finally, we provide informal user feedback regarding the WADE module that demonstrates its analysis capabilities.
Conclusion and future work
In this paper, we extend our previous framework Aneulysis – a system to manage and visually explore aneurysms developed in close cooperation with medical and CFD experts [7]. Aneulysis is focused on clinically essential aspects, such as to find an optimal treatment, and supports the communication between engineers and physicians, e.g., in investigating how changes in flow patterns after treatment are related to its success. In this work, we integrated the exchange of exploration results, which
CRediT authorship contribution statement
Monique Meuschke: Conceptualization, Methodology, Software, Visualization, Writing - review & editing. Bernhard Preim: Funding acquisition, Writing - review & editing. Kai Lawonn: Supervision, Writing - review & editing, Funding acquisition.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgment
This work was partially funded by the Carl Zeiss Foundation. The authors would like to thank Samuel Voß and Ralph Wickenhöfer for the fruitful discussions on these and related topics.
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